非因果先验信噪比估计的LSA算法改进

对于大多数的语音增强算法,先验信噪比及背景噪音频谱估计的准确与否,对语音增强的效果影响至关重要.为此,在传统MMSE-LSA算法的基础上,提出一种基于非因果先验信噪比估计的LSA 改进算法,较好地弥补了传统 LSA 算法在先验信噪比上估计的不足,同时采用平滑系数动态更新噪音频谱值,使估计值能更好地跟踪噪音的变化.实验结...     

基于非因果先验信噪比估计的语音增强改进算法

为提高MMSE-LSA语音增强算法在低信噪比下的语音增强效果,提出一种改进的MMSE-LSA算法。该算法采用非因果先验信噪比估计法来估计先验信噪比,并引入无语音概率的思想,对增益函数进行改进。实验结果表明,相比传统MMSE-LSA算法,改进算法能更好地抑制残留噪声,提高语音的信噪比,增强效果更好。     

改进相位谱补偿的语音增强方法

针对传统单通道语音增强方法中用带噪语音相位代替纯净语音相位重建时域信号，使得语音主观感知质量改善受限的情况，提出了一种改进相位谱补偿的语音增强算法。该算法提出了基于每帧语音输入信噪比的Sigmoid型相位谱补偿函数，能够根据噪声的变化来灵活地对带噪语音的相位谱进行补偿；结合改进DD的先验信噪比估计与语音存在概率算法（SPP）来估计噪声功率谱；在维纳滤波中结合新的语音存在概率噪声功率谱估计与相位谱补偿来提高语音的增强效果。相比传统相位谱补偿（PSC）算法而言，改进算法可以有效抑制音频信号中的各类噪声，同时增强语音信号感知质量，提升语音的可懂度。     

基于先验信噪比估计和增益平滑的语音增强

针对语音增强算法残留“音乐噪声”的问题,分析了基于先验信噪比估计的语音增强算法,并在此基础上提出自适应先验信噪比估计与增益平滑相结合的方法.这种方法先对先验信嗓比进行估计,然后对增益函数进行平滑,减小相邻增益函数的随机跳变,弥补了传统先验信噪比估计的不足.最后对含高斯白噪声的语音信号进行处理,仿真结果表明,该算法在抑制“音乐噪声”的效果上得到一定改善,提高了语音增强的性能.     

谐波重构先验信噪比估计算法

先验信噪比单通道语音增强算法在信噪比较高时能有效地去除噪声,但在信噪比较低时语音高次谐波失真较为严重。针对此提出了一种基于谐波重构的先验信噪比估计算法,对增强后的信号加权求平方,进行功率谱的二次谱处理,以加强语音信号的周期性;再进行谐波重构,提升谐波分量。实验研究表明,该算法在低信噪比时能够有效地增强语音谐波分量,相对于先验信噪比估计的语音增强算法能够改善语音质量,减少语音失真。     

新型几何谱减语音增强方法

针对几何谱减算法在处理快速变化的语音时产生语音畸变的缺点,提出一种基于最小均方误差算法估计每帧语音信号的每一个频率分量上的平滑系数,产生自适应帧频率分量平滑系数代替固定值的平滑系数来估计先验信噪比,从而得到更加接近于真实情况的先验信噪比。通过计算板仓-斋藤距离,及利用仿真波形图、语谱图对算法进行客观测试,结果表明新算法相对其他谱减法在相同的去噪度下,语音畸变度最小且几乎察觉不到音乐噪声;特别是在低信噪比非平稳环境下,相对其他谱减法的优势更加显著。     

一种新的强噪声环境下的语音增强算法

针对强噪声环境下语音增强中噪声估计和先验信噪比估计算法导致的语音失真和音乐噪声的问题,利用语音和噪声的统计模型的对称性得到一种噪声幅度的估计值为参考,提出了一种噪声估计算法,改进了先验信噪比估计算法,形成了一种新的增强算法,适用于强噪声环境下的语音增强。由仿真实验给出的客观评分看出,在0 dB乃至－5 dB条件下,给出信噪比估计算法能够有效减小信号失真,基本上没有残留音乐噪声。     

基于掩蔽估计与优化的单通道语音增强算法

单通道语音增强算法通过从带噪语音中估计并抑制噪声成分来得到增强语音。然而，噪声估计算法在计算时存在过估现象，导致部分估计噪声能量值比实际值大。尽管可以通过补偿消去这些过估值，但引入的误差同样会降低增强语音的整体质量。针对此问题，提出一种基于计算听觉场景分析（CASA）的时频掩蔽估计与优化算法。首先，通过直接判决（DD）算法估计先验信噪比（SNR）并计算初始掩蔽；其次，利用噪声与带噪语音在Gammatone频带内的互相关（ICC）系数来计算噪声的存在概率，结合带噪语音能量谱得到新的噪声估计，减少原估计噪声中的过估成分；然后，利用优化算法对初始掩蔽进行迭代处理以减少其中因噪声过估而存在的误差并增加其中的目标语音成分，在满足条件后停止迭代并得到新的掩蔽；最后，利用新的掩蔽合成增强语音。实验结果表明在不同的背景噪声下，相比优化前，新的掩蔽使增强语音获得了较高的主观语音质量（PESQ）和语音可懂度（STOI）值，提升了语音听感与可懂度。     

改进增益函数的MMSE语音增强算法

讨论了基于语音短时对数谱最小均方误差(MMSE-STSA)的语音增强算法,将先验信噪比估计引入增益函数的计算中,有效消除噪声.在带噪信号模型中引入语音存在的不确定度,估计出每个频点的先验无声概率,对增益函数进行改进.通过客观与主观两种评价方法将改进算法与小波变换算法和MMSE估计算法进行比较,实验结果表明,改进算法能更好地抑制背景噪声并且使增强后的语音有较小的失真,增加语音清晰度和理解度.     

一种基于谱减的语音增强算法改进

为进一步降低噪声对采集语音的干扰,提出了一种新的谱减改进方法。采用阈值法对非平稳背景噪声信号进行估计,计算出先验信噪比,得到还原的纯净语音信号。用MATLAB实现了整个算法的仿真,并与传统谱减法结果相比较,仿真结果表明,该算法对非平稳噪声追踪性较好,在抑制背景噪声,减少音乐噪声前提下,提高了语音的可懂度,其计算复杂度也可以接受。     

A novel fast nonstationary noise tracking approach based on MMSE spectral power estimator

Estimating the noise power spectral density (PSD) from the corrupted speech signal is an essential component for speech enhancement algorithms. In this paper, a novel noise PSD estimation algorithm based on minimum mean-square error (MMSE) is proposed. The noise PSD estimate is obtained by recursively smoothing the MMSE estimation of the current noise spectral power. For the noise spectral power estimation, a spectral weighting function is derived, which depends on the a priori signal-to-noise ratio (SNR). Since the speech spectral power is highly important for the a priori SNR estimate, this paper proposes an MMSE spectral power estimator incorporating speech presence uncertainty (SPU) for speech spectral power estimate to improve the a priori SNR estimate. Moreover, a bias correction factor is derived for speech spectral power estimation bias. Then, the estimated speech spectral power is used in “decision-directed” (DD) estimator of the a priori SNR to achieve fast noise tracking. Compared to three state-of-the-art approaches, i.e., minimum statistics (MS), MMSE-based approach, and speech presence probability (SPP)-based approach, it is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR conditions. When employed in a speech enhancement system, improved speech enhancement performances in terms of segmental SNR improvements (SSNR+) and perceptual evaluation of speech quality (PESQ) can be observed.     

Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise, but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore, the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called two-step noise reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the reverberation effect. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in enhanced speech because of the unreliability of estimators for small signal-to-noise ratios. This is mainly due to the difficult task of noise power spectrum density (PSD) estimation in single-microphone schemes. To overcome this problem, we propose a method called harmonic regeneration noise reduction (HRNR). A nonlinearity is used to regenerate the degraded harmonics of the distorted signal in an efficient way. The resulting artificial signal is produced in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics. These methods are analyzed and objective and formal subjective test results between HRNR and TSNR techniques are provided. A significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics.     

基于大规模MIMO的散射信道估计技术

随着用户对通信速率的要求日益增长,散射通信的通信容量亟待提升。大规模多输入多输出（MIMO）技术是提升容量的一种重要途径,本文研究基于大规模MIMO的对流层散射通信系统的信道估计问题。首先建立基于二维均匀方形天线阵列的大规模MIMO对流层散射信道模型,其次提出一种信道协方差矩阵估计算法对传统最小均方差（MMSE）信道估计算法进行改进,最后与最小二乘（LS）、传统MMSE算法和理想MMSE信道估计算法的准确度进行对比。仿真结果表明：在信噪比（SNR）为0~25 dB的情况下,传统的MMSE算法的准确度相较于LS算法的提升效果并不明显,与理想MMSE算法的准确度有一定差距;但改进MMSE信道估计算法的准确性优于传统MMSE算法,同等条件下NMSE相同时,其SNR可提升3~5 dB,并随着SNR的增大逐渐逼近理想MMSE算法。     

基于子频带加权的语音活动检测算法

为了降低噪声及决策导向(DD)参数估计算法的帧延迟特性对语音活动检测（VAD）算法鲁棒性的影响,首先采用两步降噪(TSNR)技术估计算法提高语音瞬变时刻参数估计准确性,并针对语音噪声的频率选择性,通过频带分割,将噪声污染限制到孤立子频带中,构建了由子频带特征与可靠性因子结合提供判别结果的子频带加权VAD算法。实验表明,此子频带加权算法优于Sohn算法、Cho算法以及G.729B等全频带算法。     

Iterative SNR estimation using a priori information

A class iterative signal-to-noise ratio (SNR) estimation algorithm is proposed in this paper. The data samples are governed by a given distribution with a priori. The expectation maximization (EM) algorithm is applied to iteratively maximize the likelihood function so as to realize the SNR estimation. Cramer–Rao bounds (CRB) with different a priori are compared for binary phase shift keying and orthogonal phase shift keying systems, which show the potential of the SNR estimator in turbo-like systems. In high-order modulations, simulation results show that the reduced-complexity iterative method with equal a priori has better performance in middle or high SNR region than the foregone ones. Moreover, the new method with feedback information is the best when its iteration number is 4 and extrinsic information larger than 0.4. These methods are applied in the bit-interleaved coded modulation with iterative decode (BICM-ID) system to validate the effect of the proposed methods.     

改进的基于数据辅助的MPSK信号信噪比估计

针对目前数据辅助的信噪比估计方法假设信道系数为实数这一问题,基于最大似然序列估计准则,给出了信道系数为复数时MPSK调制信号的信噪比估计方法.根据利用的数据不同,给出了3种形式,分别为只利用训练序列、硬判决辅助、软判决辅助的方法.通过理论分析和仿真结果比较了改进方法和原来方法的性能,分析了当信道系数的相位不同、先验信息不同时各种方法的性能.实验结果表明,当先验信息质量较高时,软判决辅助的改进方法的估计精度非常高,且适用的信噪比范围也较大;当先验信息质量较低时,只利用训练序列的改进方法也可以得到比较精确地估计结果.     

Simultaneous Detection and Estimation Approach for Speech Enhancement

In this paper, we present a simultaneous detection and estimation approach for speech enhancement. A detector for speech presence in the short-time Fourier transform domain is combined with an estimator, which jointly minimizes a cost function that takes into account both detection and estimation errors. Cost parameters control the tradeoff between speech distortion, caused by missed detection of speech components and residual musical noise resulting from false-detection. Furthermore, a modified decision-directed a priori signal-to-noise ratio (SNR) estimation is proposed for transient-noise environments. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach with the proposed a priori SNR estimator, which facilitate suppression of transient noise with a controlled level of speech distortion.     

Estimators of The Magnitude-Squared Spectrum and Methods for Incorporating SNR Uncertainty

Statistical estimators of the magnitude-squared spectrum are derived based on the assumption that the magnitude-squared spectrum of the noisy speech signal can be computed as the sum of the (clean) signal and noise magnitude-squared spectra. Maximum a posterior (MAP) and minimum mean square error (MMSE) estimators are derived based on a Gaussian statistical model. The gain function of the MAP estimator was found to be identical to the gain function used in the ideal binary mask (IdBM) that is widely used in computational auditory scene analysis (CASA). As such, it was binary and assumed the value of 1 if the local SNR exceeded 0 dB, and assumed the value of 0 otherwise. By modeling the local instantaneous SNR as an F-distributed random variable, soft masking methods were derived incorporating SNR uncertainty. The soft masking method, in particular, which weighted the noisy magnitude-squared spectrum by the a priori probability that the local SNR exceeds 0 dB was shown to be identical to the Wiener gain function. Results indicated that the proposed estimators yielded significantly better speech quality than the conventional MMSE spectral power estimators, in terms of yielding lower residual noise and lower speech distortion.     

Speech enhancement based on undecimated wavelet packet-perceptual filterbanks and MMSE–STSA estimation in various noise environments

In this paper, we proposed a new speech enhancement system, which integrates a perceptual filterbank and minimum mean square error–short time spectral amplitude (MMSE–STSA) estimation, modified according to speech presence uncertainty. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree, according to critical bands of psycho-acoustic model of human auditory system. The MMSE–STSA estimation (modified according to speech presence uncertainty) was used for estimation of speech in undecimated wavelet packet domain. The perceptual filterbank provides a good auditory representation (sufficient frequency resolution), good perceptual quality of speech and low computational load. The MMSE–STSA estimator is based on a priori SNR estimation. A priori SNR estimation, which is a key parameter in MMSE–STSA estimator, was performed by using “decision directed method.” The “decision directed method” provides a trade off between noise reduction and signal distortion when correctly tuned. The experiments were conducted for various noise types. The results of proposed method were compared with those of other popular methods, Wiener estimation and MMSE–log spectral amplitude (MMSE–LSA) estimation in frequency domain. To test the performance of the proposed speech enhancement system, three objective quality measurement tests (SNR, segSNR and Itakura–Saito distance (ISd)) were conducted for various noise types and SNRs. Experimental results and objective quality measurement test results proved the performance of proposed speech enhancement system. The proposed speech enhancement system provided sufficient noise reduction and good intelligibility and perceptual quality, without causing considerable signal distortion and musical background noise.